Search: onr:"swepub:oai:DiVA.org:bth-20122" >
E-mail classificati...
E-mail classification with machine learning and word embeddings for improved customer support
-
- Borg, Anton (author)
- Blekinge Tekniska Högskola,Institutionen för datavetenskap
-
- Boldt, Martin (author)
- Blekinge Tekniska Högskola,Institutionen för datavetenskap
-
- Rosander, Oliver (author)
- Blekinge Tekniska Högskola,Institutionen för datavetenskap,student
-
show more...
-
- Ahlstrand, Jim (author)
- Blekinge Tekniska Högskola,Institutionen för datavetenskap,student
-
show less...
-
(creator_code:org_t)
- 2020-06-19
- 2021
- English.
-
In: Neural Computing & Applications. - : Springer. - 0941-0643 .- 1433-3058. ; 33:6, s. 1881-1902
- Related links:
-
https://doi.org/10.1...
-
show more...
-
https://bth.diva-por... (primary) (Raw object)
-
https://link.springe...
-
https://urn.kb.se/re...
-
https://doi.org/10.1...
-
show less...
Abstract
Subject headings
Close
- Classifying e-mails into distinct labels can have a great impact on customer support. By using machine learning to label e-mails, the system can set up queues containing e-mails of a specific category. This enables support personnel to handle request quicker and more easily by selecting a queue that match their expertise. This study aims to improve a manually defined rule-based algorithm, currently implemented at a large telecom company, by using machine learning. The proposed model should have higher F1-score and classification rate. Integrating or migrating from a manually defined rule-based model to a machine learning model should also reduce the administrative and maintenance work. It should also make the model more flexible. By using the frameworks, TensorFlow, Scikit-learn and Gensim, the authors conduct a number of experiments to test the performance of several common machine learning algorithms, text-representations, word embeddings to investigate how they work together. A long short-term memory network showed best classification performance with an F1-score of 0.91. The authors conclude that long short-term memory networks outperform other non-sequential models such as support vector machines and AdaBoost when predicting labels for e-mails. Further, the study also presents a Web-based interface that were implemented around the LSTM network, which can classify e-mails into 33 different labels. © 2020, The Author(s).
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Språkteknologi (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Language Technology (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- E-mail classification
- Long short-term memory
- Machine learning
- Natural language processing
- Adaptive boosting
- Brain
- Electronic mail
- Embeddings
- Learning systems
- Multimedia systems
- Support vector machines
- Classification performance
- Classification rates
- Email classification
- Machine learning models
- Rule based algorithms
- Rule-based models
- Text representation
- Web-based interface
Publication and Content Type
- ref (subject category)
- art (subject category)
Find in a library
To the university's database